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Data Summarization

Data Summarization is a central problem in the area of machine learning, where we want to compute a small summary of the data.

Source: How to Solve Fair k-Center in Massive Data Models

Papers

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Showing 21–30 of 97 papers

TitleStatusHype
Data Summarization beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization—0
Deep Submodular Networks for Extractive Data Summarization—0
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"—0
A Unified Framework for Task-Driven Data Quality Management—0
Differentiable Submodular Maximization—0
Differentially Private Distributed Data Summarization under Covariate Shift—0
Data Summarization at Scale: A Two-Stage Submodular Approach—0
Differentially Private Submodular Maximization: Data Summarization in Disguise—0
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach—0
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